Adaptive solution of nonlinear control problems is required in such fields as robotics, automation, filtering, and time series prediction. The solution is generally in the form of a trajectory, that is, a variable or set of variables whose values change as a function of time. In order to compute the trajectory, it has been suggested that recurrent neural networks be used because of their ability to learn from examples. However, the architecture of such recurrent networks has shown significant limitations in reproducing relatively simple trajectories such as a circle and a figure eight.